Royal Knit, Inc. GOBHRF235-01-10 ROYLA FLUSH

FeetMe Evaluation mobileAndroid application version 3.Y.Z.

Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.

Dental imaging capture, enhance and display software.

Handpiece Assembly

• 3 Ply - Disposable High Filtration Face Mask• Face Mask Size: 170mm x 90mm (± 5 mm) • Non - sterile, single use• Color White• 50 masks pack in one box• Box dimensions: 185x105x90mm

Virtual Field VF3 is a portable virtual reality visual field device. It runs a variety of visual field patterns, including but not limited to 10-2, 24-2, and 30-2, as well as multiple strategies, from suprathreshold screenings to full threshold tests. It also includes a color vision screening exam.

FeetMe Evaluation mobileAndroid application version 3.Y.Z.

Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.

Dental imaging capture, enhance and display software.